GIS Application of Healthcare Data for Advancing Epidemiological Studies

2011 ◽

pp. 362-377

Author(s):

Joseph M. Woodside ◽

Iftikhar U. Sikder

Keyword(s):

Epidemiological Studies ◽

Space Time ◽

Healthcare Management ◽

Scan Statistics ◽

Healthcare Data ◽

Gis Applications ◽

Disease Patterns ◽

Utilization Patterns ◽

Improve Analysis ◽

Service Data

Healthcare practices increasingly rely on advanced technologies to improve analysis capabilities for decision making. In particular, spatial epidemiological approach to healthcare studies provides significant insight in evaluating health intervention and decisions through Geographic Information Systems (GIS) applications. This chapter illustrates a space-time cluster analysis using Kulldorff’s Scan Statistics (1999), local indicators of spatial autocorrelation, and local G-statistics involving routine clinical service data as part of a limited data set collected by a Northeast Ohio healthcare organization over a period 1994 – 2006. The objective is to find excess space and space-time variations of lung cancer and to identify potential monitoring and healthcare management capabilities. The results were compared with earlier research (Tyczynski & Berkel, 2005); similarities were noted in patient demographics for the targeted study area. The findings also provide evidence that diagnosis data collected as a result of rendered health services can be used in detecting potential disease patterns and/or utilization patterns, with the overall objective of improving health outcomes.

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Space-Time Cluster Analysis

Health Information Systems ◽

10.4018/978-1-60566-988-5.ch119 ◽

2011 ◽

pp. 1842-1856

Author(s):

Joseph M. Woodside ◽

Iftikhar U. Sikder

Keyword(s):

Cluster Analysis ◽

Space Time ◽

Healthcare Management ◽

Scan Statistics ◽

Patient Demographics ◽

Disease Patterns ◽

Utilization Patterns ◽

Time Variations ◽

Service Data ◽

Management Capabilities

Spatial epidemiological approach to healthcare studies provides significant insight in evaluating health intervention and decision making. This article illustrates a space-time cluster analysis using Kulldorff’s Scan Statistics (1999), local indicators of spatial autocorrelation, and local G-statistics involving routine clinical service data as part of a limited data set collected by a Northeast Ohio healthcare organization (Kaiser Foundation Health Plan of Ohio) over a period 1994—2006. The objective is to find excess space and space - time variations of lung cancer and to identify potential monitoring and healthcare management capabilities. The results were compared with earlier research (Tyczynski, & Berkel, 2005); similarities were noted in patient demographics for the targeted study area. The findings also provide evidence that diagnosis data collected as a result of rendered health services can be used in detecting potential disease patterns and/or utilization patterns, with the overall objective of improving health outcomes.

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Space-Time Cluster Analysis

International Journal of Healthcare Information Systems and Informatics ◽

10.4018/jhisi.2009071005 ◽

2009 ◽

Vol 4 (4) ◽

pp. 69-82 ◽

Cited By ~ 1

Author(s):

Joseph M. Woodside ◽

Iftikhar U. Sikder

Keyword(s):

Cluster Analysis ◽

Space Time ◽

Healthcare Management ◽

Scan Statistics ◽

Data Set ◽

Patient Demographics ◽

Disease Patterns ◽

Utilization Patterns ◽

Service Data ◽

Management Capabilities

Spatial epidemiological approach to healthcare studies provides significant insight in evaluating health intervention and decision making. This article illustrates a space-time cluster analysis using Kulldorff’s Scan Statistics (1999), local indicators of spatial autocorrelation, and local G-statistics involving routine clinical service data as part of a limited data set collected by a Northeast Ohio healthcare organization (Kaiser Foundation Health Plan of Ohio) over a period 1994—2006. The objective is to find excess space and space - time variations of lung cancer and to identify potential monitoring and healthcare management capabilities. The results were compared with earlier research (Tyczynski, & Berkel, 2005); similarities were noted in patient demographics for the targeted study area. The findings also provide evidence that diagnosis data collected as a result of rendered health services can be used in detecting potential disease patterns and/or utilization patterns, with the overall objective of improving health outcomes.

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Spatial and Temporal Distribution of Tuberculosis in the State of Mexico, Mexico

The Scientific World JOURNAL ◽

10.1100/2012/570278 ◽

2012 ◽

Vol 2012 ◽

pp. 1-7 ◽

Cited By ~ 7

Author(s):

Adrian Zaragoza Bastida ◽

Marivel Hernández Tellez ◽

Lilia P. Bustamante Montes ◽

Imelda Medina Torres ◽

Jaime Nicolás Jaramillo Paniagua ◽

...

Keyword(s):

Large Population ◽

Temporal Distribution ◽

Epidemiological Studies ◽

Space Time ◽

The State ◽

Scan Statistics ◽

World Health ◽

Spatial And Temporal Distribution ◽

Clinical Variants ◽

Health Organization

Tuberculosis (TB) is one of the oldest human diseases that still affects large population groups. According to the World Health Organization (WHO), there were approximately 9.4 million new cases worldwide in the year 2010. In Mexico, there were 18,848 new cases of TB of all clinical variants in 2010. The identification of clusters in space-time is of great interest in epidemiological studies. The objective of this research was to identify the spatial and temporal distribution of TB during the period 2006–2010 in the State of Mexico, using geographic information system (GIS) and SCAN statistics program. Nine significant clusters () were identified using spatial and space-time analysis. The conclusion is that TB in the State of Mexico is not randomly distributed but is concentrated in areas close to Mexico City.

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Online platform for applying space–time scan statistics for prospectively detecting emerging hot spots of dengue fever

International Journal of Health Geographics ◽

10.1186/s12942-016-0072-6 ◽

2016 ◽

Vol 15 (1) ◽

Cited By ~ 7

Author(s):

Chien-Chou Chen ◽

Yung-Chu Teng ◽

Bo-Cheng Lin ◽

I-Chun Fan ◽

Ta-Chien Chan

Keyword(s):

Dengue Fever ◽

Hot Spots ◽

Space Time ◽

Scan Statistics ◽

Online Platform ◽

Time Scan

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Exploring Factors Influencing Big Data and Analytics Adoption in Healthcare Management

Handbook of Research on Managing Information Systems in Developing Economies - Advances in Information Quality and Management ◽

10.4018/978-1-7998-2610-1.ch020 ◽

2020 ◽

pp. 413-428

Author(s):

Sampson Abeeku Edu ◽

Divine Q. Agozie

Keyword(s):

Big Data ◽

Data Management ◽

Patient Management ◽

Technological Development ◽

Healthcare Management ◽

Quality Cost ◽

Healthcare Data ◽

Management Quality ◽

Factors Influencing ◽

Quality Decision

Demand for improvement in healthcare management in the areas of quality, cost, and patient care has been on the upsurge because of technology. Incessant application and new technological development to manage healthcare data significantly led to leveraging on the use of big data and analytics (BDA). The application of the capabilities from BDA has provided healthcare institutions with the ability to make critical and timely decisions for patients and data management. Adopting BDA by healthcare institutions hinges on some factors necessitating its application. This study aims to identify and review what influences healthcare institutions towards the use of business intelligence and analytics. With the use of a systematic review of 25 articles, the study identified nine dominant factors driving healthcare institutions to BDA adoption. Factors such as patient management, quality decision making, disease management, data management, and promoting healthcare efficiencies were among the highly ranked factors influencing BDA adoption.

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For Better Healthcare Mining Health Data

Advances in Data Mining and Database Management - Intelligent Multidimensional Data Clustering and Analysis ◽

10.4018/978-1-5225-1776-4.ch006 ◽

2017 ◽

pp. 135-158

Author(s):

Güney Gürsel

Keyword(s):

Data Mining ◽

Nearest Neighbor ◽

Healthcare Management ◽

Support Vector ◽

Privacy And Security ◽

Healthcare Data ◽

Management Fraud ◽

Use Of Data ◽

Customer Relation Management ◽

Vector Machines

Data mining has great contributions to the healthcare such as support for effective treatment, healthcare management, customer relation management, fraud and abuse detection and decision making. The common data mining methods used in healthcare are Artificial Neural Network, Decision trees, Genetic Algorithms, Nearest neighbor method, Logistic regression, Fuzzy logic, Fuzzy based Neural Networks, Bayesian Networks and Support Vector Machines. The most used task is classification. Because of the complexity and toughness of medical domain, data mining is not an easy task to accomplish. In addition, privacy and security of patient data is a big issue to deal with because of the sensitivity of healthcare data. There exist additional serious challenges. This chapter is a descriptive study aimed to provide an acquaintance to data mining and its usage and applications in healthcare domain. The use of Data mining in healthcare informatics and challenges will be examined.

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Space–time patterns ofCampylobacterspp. colonization in broiler flocks, 2002–2006

Epidemiology and Infection ◽

10.1017/s0950268810000051 ◽

2010 ◽

Vol 138 (9) ◽

pp. 1336-1345 ◽

Cited By ~ 7

Author(s):

M. E. JONSSON ◽

M. NORSTRÖM ◽

M. SANDBERG ◽

A. K. ERSBØLL ◽

M. HOFSHAGEN

Keyword(s):

Risk Factors ◽

Relative Risk ◽

Function Analysis ◽

Space Time ◽

Scan Statistics ◽

Spatial Scan Statistics ◽

Factors Associated ◽

Time Patterns ◽

Broiler Flock ◽

Spatial Scan

SUMMARYThis study was performed to investigate space–time patterns ofCampylobacterspp. colonization in broiler flocks in Norway. Data on theCampylobacterspp. status at the time of slaughter of 16 054 broiler flocks from 580 farms between 2002 and 2006 was included in the study. Spatial relative risk maps together with maps of space–time clustering were generated, the latter by using spatial scan statistics. These maps identified the same areas almost every year where there was a higher risk for a broiler flock to test positive forCampylobacterspp. during the summer months. A modifiedK-function analysis showed significant clustering at distances between 2·5 and 4 km within different years. The identification of geographical areas with higher risk forCampylobacterspp. colonization in broilers indicates that there are risk factors associated withCampylobacterspp. colonization in broiler flocks varying with region and time, e.g. climate, landscape or geography. These need to be further explored. The results also showed clustering at shorter distances indicating that there are risk factors forCampylobacterspp. acting in a more narrow scale as well.

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Retrospective space–time analysis methods to support West Nile virus surveillance activities

Epidemiology and Infection ◽

10.1017/s0950268814000442 ◽

2014 ◽

Vol 143 (1) ◽

pp. 202-213 ◽

Cited By ~ 6

Author(s):

P. MULATTI ◽

M. MAZZUCATO ◽

F. MONTARSI ◽

S. CIOCCHETTA ◽

G. CAPELLI ◽

...

Keyword(s):

West Nile Virus ◽

Linear Models ◽

Space Time ◽

Scan Statistics ◽

Limited Area ◽

Entomological Surveillance ◽

West Nile ◽

Spatial And Temporal Scales ◽

Virus Surveillance ◽

Human West Nile Virus

SUMMARYThe steep increase in human West Nile virus (WNV) infections in 2011–2012 in north-eastern Italy prompted a refinement of the surveillance plan. Data from the 2010–2012 surveillance activities on mosquitoes, equines, and humans were analysed through Bernoulli space–time scan statistics, to detect the presence of recurrent WNV infection hotspots. Linear models were fit to detect the possible relationships between WNV occurrence in humans and its activity in mosquitoes. Clusters were detected for all of the hosts, defining a limited area on which to focus surveillance and promptly identify WNV reactivation. Positive relationships were identified between WNV in humans and in mosquitoes; although it was not possible to define precise spatial and temporal scales at which entomological surveillance could predict the increasing risk of human infections. This stresses the necessity to improve entomological surveillance by increasing both the density of trapping sites and the frequency of captures.

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